Gated attention neural networks

1. A system comprising one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to implement an attention neural network that is configured to receive an input sequence and to process the input sequence to generate an output, the attention neural network comprising:

an attention block configured to receive a query input, a key input, and a value input that are derived from an attention block input, the attention block comprising:

an attention neural network layer configured to:

receive an attention layer input derived from the query input, the key input, and the value input, and

apply an attention mechanism to the attention layer input to generate an attention layer output for the attention neural network layer; and

a gating neural network layer configured to apply a gating mechanism to the attention block input and the attention layer output of the attention neural network layer to generate a gated attention output.

2. The system of claim 1 , wherein the attention block further comprises a first layer normalization layer configured to apply a layer normalization operation to the query input, the key input, and the value input to generate a normalized query input, a normalized key input, and a normalized value input, and wherein the attention layer input comprises the normalized query input, the normalized key input, and the normalized value input.

3. The system of claim 1 , wherein applying the gating mechanism on the attention block input and the attention layer output comprises:

applying a sigmoid modulation to the attention block input to generate a first sigmoid modulated output; and

combining the first sigmoid modulated output with the attention layer output to generate the gated attention output.

4. The system of claim 1 , wherein applying the gating mechanism on the attention block input and the attention layer output comprises:

applying a sigmoid modulation to the attention layer output to generate a second sigmoid modulated output, and

combining the second sigmoid modulated output with the attention block input to generate the gated attention output.

5. The system of claim 1 , wherein applying the gating mechanism on the attention block input and the attention layer output comprises: computing a convex combination of the attention block input and the attention layer output using a sigmoid weighting to generate the gated attention output.

6. The system of claim 1 , wherein applying the gating mechanism on the attention block input and the attention layer output comprises:

applying a sigmoid and a tanh activation on the attention layer output to generate a sigmoid-tanh output, and

combining the sigmoid-tanh output with the attention block input to generate the gated attention output.

7. The system of claim 1 , wherein applying the gating mechanism on the attention block input and the attention layer output comprises:

applying a gated recurrent unit on the attention block input and the attention layer output.

8. The system of claim 1 , wherein the attention block further comprise:

a second layer normalization layer configured to apply a layer normalization operation to the gated attention output to generate a normalized-gated attention output,

one or more feedforward neural network layers configured to apply one or more transformations to the normalized-gated attention output to generate a temporary attention block output, and

a second gating neural network layer configured to apply a second gating mechanism to the temporary attention block output and the gated attention output to generate a final attention block output for the attention block.

9. The system of claim 1 , wherein the attention mechanism is a self-attention mechanism.

10. The system of claim 1 , wherein the attention mechanism is a masked self-attention mechanism.